HIV, sexual violence, and termination of pregnancy among adolescent and adult female sex workers in Malawi: A respondent-driven sampling study

Female Sex Workers (FSWs) are a hard-to-reach and understudied population, especially those who begin selling sex at a young age. In one of the most economically disadvantaged regions in Malawi, a large population of women is engaged in sex work surrounding predominantly male work sites and transport routes. A cross-sectional study in February and April 2019 in Nsanje district used respondent driven sampling (RDS) to recruit women ≥13 years who had sexual intercourse (with someone other than their main partner) in exchange for money or goods in the last 30 days. A standardized questionnaire was filled in; HIV, syphilis, gonorrhea, and chlamydia tests were performed. CD4 count and viral load (VL) testing occurred for persons living with HIV (PLHIV). Among 363 study participants, one-quarter were adolescents 13–19 years (25.9%; n = 85). HIV prevalence was 52.6% [47.3–57.6] and increased with age: from 14.7% (13–19 years) to 87.9% (≥35 years). HIV status awareness was 95.2% [91.3–97.4], ART coverage was 98.8% [95.3–99.7], and VL suppression 83.2% [77.1–88.0], though adolescent FSWs were less likely to be virally suppressed than adults (62.8% vs. 84.4%). Overall syphilis prevalence was 29.7% [25.3–43.5], gonorrhea 9.5% [6.9–12.9], and chlamydia 12.5% [9.3–16.6]. 72.4% had at least one unwanted pregnancy, 17.9% had at least one abortion (40.1% of which were unsafe). Half of participants reported experiencing sexual violence (SV) (47.6% [42.5–52.7]) and more than one-tenth (14.2%) of all respondents experienced SV perpetrated by a police officer. Our findings show high levels of PLHIV-FSWs engaged in all stages of the HIV cascade of care. The prevalence of HIV, other STIs, unwanted pregnancy, unsafe abortion, and sexual violence remains extremely high. Peer-led approaches contributed to levels of ART coverage and HIV status awareness similar to those found in the general district population, despite the challenges and risks faced by FSWs.

HIV prevalence estimates over waves of recruitment, overall and by sites (Figure 2A), and for each seed ( Figure  2B). Site 1: Fatima, Site 2: Bangula, Site 3: Nsanje Boma Convergence plots: The overall RDS-II HIV prevalence estimates appeared to converge (Figure 2A). At the start of recruitment, the prevalence estimate was 60%, which dropped to 45% around the 100th recruit (one-third of the overall sample) and then steadily approached the final estimate of 52% (indicated by the dotted line) at the end of recruitment. However, estimate equilibrium was not yet reached as the line showed a steady upward trend and no sign of flattening out. This was also true for site 2, but not for site 1 and site 3. Here, the lines flattened out midway (site 1) or two-thirds of the way (site 3) through recruitment process indicating equilibrium reached and the estimate has stabilised.
Bottleneck plots: Bottleneck plots of the RDS-II HIV prevalence estimates showed important changes in estimates over the course of recruitment ( Figure 2B). In general, recruitment chains whose prevalence estimates started at 100% dropped steeply as recruitment progressed and HIV negatives were enrolled and vice versa for recruitment chains starting with 0% prevalence. However, not all chains converged towards the overall RDS-II population estimate of 52% and several bottlenecks were visible, especially several chains stabilising at higher prevalence estimates due to site variation. Age group estimates over waves of recruitment, overall and by sites ( Figure 3A), and for each seed ( Figure 3B).

Site 1: Fatima, Site 2: Bangula, Site 3: Nsanje Boma
Convergence plots: Convergence analysis was performed obtaining the RDS-II estimates for the proportion of FSW aged 27-55 years. Estimate equilibrium was attained relatively early ( Figure 3A). At the start, the estimate was 60%, and as the number of participants increased, the estimate dropped to 45% and then increased to 50% at the end of recruitment. Both site 1 and 2 recruited older FSW at the start, hence estimate was close to 80% but then dropped quickly and stabilised to 50% as younger FSWs were recruited. Site 3 recruited a more diverse age range since the start, as initial estimate of clients aged 27-55 years was 40% which steadily increased and stabilised at 50%.
Bottleneck plots: The bottleneck plots revealed recruitment chains in site 1 and site 2 starting with older FSW but recruitment quickly crossed over to the younger age groups as lines descended steeply from 100% ( Figure 3B). In site 3, there was cross-over in both directions from older to younger and younger to older. Across all sites, there were a couple of chains that remained caught within the younger age group (1 in site 1, 1 in site 2 and 2 in site 3). Ever enrolled in MSF estimates over waves of recruitment, overall and by sites ( Figure 4A), and for each seed ( Figure 4B). Site 1: Fatima, Site 2: Bangula, Site 3: Nsanje Boma

c/Ever enrolled in MSF activities
Convergence plots: Convergence analysis was also performed obtaining the RDS-II estimates for the proportion of FSWs who were ever enrolled in MSF activities ( Figure 4A). At the start, the estimate was close to 75%, and as the number of participants increased, the estimate dropped to 50%. Both site 1 and 2 recruited more FSWs who were ever enrolled in MSF activities since the start (in site 2, all seeds were previously enrolled with MSF). Site